AI is becoming part of the software delivery lifecycle, not just a tool for generating code. Development teams now use AI to support implementation, testing, infrastructure configuration, dependency selection, code review, debugging, and increasingly autonomous engineering tasks.

As this adoption grows, security risk can move through development faster as well. Vulnerable code, excessive permissions, insecure configurations, unverified dependencies, and unsafe agent actions can reach production before existing review and assurance processes catch them. The challenge is not that AI creates entirely new classes of vulnerabilities. It is that AI can increase the speed, scale, and opacity with which familiar weaknesses enter software systems.

Recent incidents show that these failures often extend beyond the code itself. Weak access controls, insecure infrastructure, compromised software supply chains, missing audit trails, and inadequate failure handling all point to a broader problem: security controls must scale with AI-assisted delivery.

In this whitepaper we have examined security vulnerabilities in AI-based software development using the OWASP Top 10:2025 as the analytical guide. Furthermore, we have connected each vulnerability category to documented incidents, identified recurring delivery failures, and correlated them with the MatrixTribe GRACE Framework. In the end you can access the risk-based control model we developed for applying stronger review, access, testing, traceability, and release controls where AI-assisted changes create greater security exposure.